T4K S3 Fuse Plugin performance

This page describes how to measure the overhead from the TVKs S3 Plugin

Measuring Trilio S3 FUSE Plugin overhead

Everything being equal, there needs to be a way to measure the overhead of TVKs FUSE implementation. Since T4K is a software-only solution, we cannot measure FUSE implementation overhead in absolute numbers. The overhead depends on various factors, so we need to establish a baseline first.

In our experiment, we provisioned two identical servers, both running CentOS. We name these serverscompute1 and compute2. A dedicated 1 GB network connects the two servers to minimize interference from other traffic. compute1 is the client and compute2 is the server. A server in our context runs an s3 endpoint and an NFS endpoint. MinIO, perhaps the most easy-to-use s3 implementation, can be launched with a simple command, and hence we will useminioas our object-store.

We provision two directories on the same disk oncompute2. /mnt/nfs_share is exported as the NFS share and/mnt/miniodatais used forminioservice for storing all objects. compute1 mounts /mnt/nfs_share and also runs the S3 FUSE plugin and provides a mount point /mnt/miniomnt. Our objective is to measure our S3 FUSE overhead w.r.t to NFS and AWS S3 API.

As part of the experiment, we create a 100GB file. 100 GB is big enough to smooth out the fixed costs associated with protocols. Moreover, most of the backup images tend to be large, and 100 GB is a good data sampling for this experiment.

First, we copy 100GB to NFS share. The throughput we achieved is 2G/min.

(mypython) [kolla@compute mnt]$ time cp 100GB /mnt/nfs_shares/

real    50m37.017s
user    0m0.462s
sys     2m54.617s

When measuring AWS S3 performance, we should avoid using multipart upload. Multipart upload performs well for large files. However, T4K FUSE plugin chunks files and manages these chunks as objects in the FUSE plugin. So we split the 100GB file into 32 MB chunks using the Linuxsplitcommand and copy individual segments into thesegmentsdirectory. We then upload the segments directory using theaws s3 cpcommand as shown below. The throughput we achieved here is 1.42GB/min, which means a 30% overhead compared to NFS results.

(mypython) [kolla@compute mnt]$ time AWS s3 cp --recursive segments/ s3://trilio/segments

real    70m24.839s
user    14m15.828s
sys     9m8.994s

Next, we copy the 100GB file to the S3 FUSE mount. The throughput achieved is very similar to theaws s3 cpcommand. Hence we can confidently conclude that the S3 FUSE implementation adds little to no overhead compared to pure aws api.

(mypython) [kolla@compute mnt]$ time cp 100GB /mnt/miniomnt/

real    71m16.633s
user    0m2.131s
sys     2m16.475s

The Trilio backup images are in QCOW2 format and the process of creating QCOW2 images is slightly different than copying a file to S3 FUSE mount. So it is prudent to measure theqemu-img convertperformance on the S3 FUSE mount. The throughput for generating QCOW2 is almost the same as copying a file to s3 FUSE mount.

(mypython) [kolla@compute mnt]$ time qemu-img convert -p -O qcow2 100GB /home/kolla/miniomnt/100GB.qcow2

real    72m36.022s
user    0m15.282s
sys     3m14.472s

In conclusion, AWS S3 API performance is subjective and varies from one object store implementation to other. The performance depends on various factors, including the number of disks, types of disks, raw processing power, replication factor, consistency, etc. Trilio S3 FUSE implementation adds little to no overhead when compared to core AWS S3 API calls. As a result, Trilio performs backups at wire transfer.